56 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" scholarships in Portugal
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the European Research Council - ERC COG 101088763. The work for this position is in the area of Machine Learning and Natural Language Processing. We are offering We offer a challenging position with the
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 months ago
skills. Have very good knowledge of machine-learning and data science methods, especially for timeseries data Have very good programming skills in programming languages such as Python. Have previous
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of the Grant are:; 1) To apply machine learning algorithms for the diagnosis of faults and malfunctions in photovoltaic plants, using data from SCADA systems combined with synthetic data from digital twins (DT
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river valleys); • Identify stylistic patterns and regional variations in schematic rock art; • Apply machine learning tools for large-scale stylistic classification; • Establish a robust chronological
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using machine learning; Interpretation of soil profiles and moisture maps. Development and validation of digital tools: Support in building georeferenced web interfaces for data visualization
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2022.09373.PTDC financed by national funds through FCT/MECI, under the following conditions: Scientific Area: Machine Learning/Recommender Systems Admission requirements: Candidates who cumulatively meet the
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will develop a new high-precision digital twin for WDS based on a self-calibrated hydraulic model with a machine learning correction model and, together with advanced data analytics, will detect and
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of scientific computing involving machine learning models for viscoelastic fluid flows. Legislation and regulations: Law Nº. 40/2004, of 18th August, in its current wording (Statutes of Scientific Research Fellow
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than 14/20 (1 point); B. Knowledge of Interactive Systems Design, Cyber-Physical Systems, Predictive Maintenance Systems, Automation, Machine Learning and Artificial Intelligence, Sensor Networks
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-vision algorithms with edge-computing processing for the automatic detection of non-conformities. Machine-learning techniques will be applied to optimize cutting parameters, and the module will be